期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：2745-2752
DOI：10.18653/v1/2021.eacl-main.237
语种：English
出版社：ACL Anthology
摘要：Most of the previous work on Event Detection (ED) has only considered the datasets with a small number of event types (i.e., up to 38 types). In this work, we present the first study on fine-grained ED (FED) where the evaluation dataset involves much more fine-grained event types (i.e., 449 types). We propose a novel method to transform the Semcor dataset for Word Sense Disambiguation into a large and high-quality dataset for FED. Extensive evaluation of the current ED methods is conducted to demonstrate the challenges of the generated datasets for FED, calling for more research effort in this area.
